{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "# import numpy.ma as ma\n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "# def bin_data(xi, yi):\n",
    "#     x = np.unique(xi)\n",
    "#     y = [None] * x.size\n",
    "#     for i in range(x.size): y[i] = yi[xi == x[i]]\n",
    "#     # Return \n",
    "#     y_mean = np.array([a.mean() for a in y])\n",
    "#     y_err = np.array([np.std(a) / (a.size**0.5) for a in y])\n",
    "#     return (x, y_mean, y_err, y, xi, yi)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "colorstyle=dict(red_face = np.array([255,85,65])/255, red_edge = np.array([201,67,52])/255,\n",
    "                blue_face = np.array([49,115,255])/255, blue_edge = np.array([36,85,189])/255,\n",
    "                green_face= np.array([84,224,81])/255, green_edge= np.array([62,166,60])/255,\n",
    "                yellow_face=np.array([255,207,49])/255,yellow_edge=np.array([191,155,36])/255,\n",
    "                gray_face=np.array([169,169,169])/255,gray_edge=np.array([137,137,137])/255)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# import 2d profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io as sio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "DataRead=sio.loadmat('FigS4AB.mat',squeeze_me=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from types import SimpleNamespace \n",
    "data1 = SimpleNamespace(**DataRead)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "boxwidth=0.71\n",
    "boxheight=0.3\n",
    "boxspacing=0.05\n",
    "boxleftspace=0.17\n",
    "\n",
    "cb_width=0.015\n",
    "cb_spacing=0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5liPnlqdLWo8ebBlnjf7S5uxy9jgsVs9oAGy3EYDMXHbJvB8r64AjofZmOvs+XU7vY8rYc2RA3JHpd2MN7cjuJrj7MLubIE8yBk+usV87/YOR/azYb7wyG/YAYXs5e+DuPW17pkx4OENA0ZG5Bg+eduy/U91+DhI0UlNQs5+2ddOHF8+fPx/r169v8FpeXh6ioqJw+fLlJq/bJKPdnFt+giAIUWFk9kh8fDxkMhni4uJq/T4/Px9RUVEICWG3OG5QLYMeVQONRoPyOlsDKU95Xkty9279zjesisfHH2evu2EDU6TzUv351QBf/nTXrvp30jxr8DwlcHzw8xT14S4jh53juMl1Iu3fny3DsVC6/n5c8PJi76J5mivxpP8zhtLcp5AjkMuAJ1jp7c3eRXMUMzJtXmY5Owfbz45jmhRrYjeAqt6P1f5Z2zwNoxISEjBgwAB4enoiOjoaAFBQUICoqCh0794dP/74Y5PXBIww2idPnsTLL7+MK1euQPh7qoMgCJBIJKgSUcknQRCEQdT0Zas5Mqbq0KVLF+zfvx9RUVFwd3fHiBEjMHToUHTu3Bk//fQTrA1wuQBGGO2ZM2di8uTJ+PHHH+HE1dSCIAjCgjDSpw0A4eHhiI+Px8iRI9GhQwcEBwdjx44dsDGiwtLgR+bm5mL58uWQSBo5WoiJH36o/6LrCaYC4ArI8RzNOU5rXMFKVto4z4xhnveJ8Dg7ZdPx1k32QixfAYcvoZjd7hkB3jwNn9kM8NX/nEo1XZhrDB/GMUeQJ/e8sJgpopbqH3rMUd3P5ebyrMxmC/Fs2i7d1ntZ2pmdm871BuZoj2CtUen9udb9jHCP/Otf/9J9P2jQIBw5cgRz587Fp59+qvv9vHnzmryuwUZ76tSp+OWXXzBhwgRDlyAIghAvNd0jBqT87dq1q9bPERER2Lt3r+5niUTSskZ75cqVePTRR/HRRx/Bu06L0p07dxq6bPNgZVW/7PCXX/Q/hidlkSM3i6cvEs9cX1YB4cmT7DV4JtewDiAA4NKRveu0vntHvwBHZ5+HZcVMGaFdN6aMZM9upkxZ1Bi9111PH2KukdmdXWHoB/bx4Z6NH1OmHaPCgmfYblouu3owpZKdFijjsCLtGVWTPBvZ0kr9VZUAkKvtxJTpcq1OQ67G/rmM3GkfOsR+zxiCwUZ7+vTpsLe3x4ABA8inTRBE68MEPm0AyMzMRFxcHM6dO4fS0tp55Ld42hLWwWCjfeTIEWRlZYkvvY8gCMIUmKg16/Tp0+Hk5IRFixbB2Zl9umGqZegDQ0NDUVpaahlG282t/os+bJj+x/BEcjj6PftYsYfTajTsYa+s3N2pU5lLcLnleJqZ8cRonVkdrCIj2YtwBO0kiQfZ63AlljPg8Bv5KdgDee8p2a4PnuZfHW6d0C/AMdi3k3cF+0Yc3czuFbPdFlUu+usMHMvZlYxFGnaufBd//ZWiAIBUztE1Rvq0qzl37hzy8/NhZ6JSeION9sSJEzFq1ChER0fX82mPHTvWaMUIgiDMiol22mFhYcjOzkZQUJBp1DL0gZs3bwYAvP/++7V+L5FIjDLaFRUVePjhh5Gfn4/iv/PYZs2ahe+//77WJ9X//vc/9OvXz+D7EARB6MVEPu2JEydizJgxiI2NRYcOtU/VhthKg412WlqaoQ/Vy9KlS+Hv74/8OqO8oqOjsYGjbLxBrl+vf8xjlbHzuH04Eqyv2LDzT3k8Baw8bdb0NACYHH6VLQR2usu5a/pzhAHgkXaM14+nUQ6Pi4on75kjhcf5uv5+zkIvdl90CUd/9Q4cQ3uzFGx3GfP9yfG6FJQ7MmU8FYwsIABKsHeQ1pUMVwzHG9jBn+0e4Zo3VjdxorEKbhPttKvzslevXl3r94ZucA3WZM+ePYiMjISvr6/ud4IgQKvVGlyemZSUhH379uHjjz/G5MmTDVWNIAjCeEzk0zb1Btdgo71kyRJcvXoVHh4eiIyMREREBLp27YoVK1YYpKRGo8HcuXOxadOmBq9/8803+Oabb+Dj44P/9//+H+bPnw8rPUESlUoFeXVEx9Oz/vGGNZSXp/uPuztTxJdjw84zuebSJf3XueLBHH1BM+6yK1y57sXKYefZRQcGsmXS000io35ylN7rtsVF7Pvw7Po5oowRPTgCp8mMiDFHiawnT/thjr7BQbfZFbLZxfpz+304pt/IOSqHHTlOO/VqMBr7XxfpYN8mDUGoycWLF1FaWoo9e/ZgwoQJuHnzJl566SVERLCPkQ2xbt06hIeHY/DgwfWuzZs3DykpKcjLy8NXX32FTz75BJ988one9d5//30EBLCncBMEQTSEYGVd66spTJkyBcmMHhbJycmYMmVKk/Uy6uPD3t4effr0QZ8+ffDSSy/hv//9Ly5evMh+YB1u3ryJTZs24fz5hv2KkTWcvn379sXixYvxzTffYP78+Y2u+fbbb2POnDlkuAmCMIjKygexJJ6RsTUZN24cxowZA09PT0RFRSE4OFg32PfatWtITExEQUFBPT83Dybd8z///PN444038NFHHzXpcUePHkVeXh7CwsIAAJWVlZDL5fD29kZ8fDweffTRWvL63CLV2NvbP8gh/6uBoa0G1PzX4+xZpsjtzuwgjSnSN7mKUnlGooCd/M+l73VGQIgjIFcVyh6ma83TKYvDJWF7lpH3zBy9AqjC+5hCFbQvZ+d733TRH+Du4sTOwc7zZQfJXTiMlSPHG8Knnf7WpmWV7ClFXAPROVxqBcra7/FSVcOBSI3mwT2bOIwdzz//PCZPnox9+/YhPj4en332Wa15ukuXLsXIkSO5bFldDDba48ePR0REBCIjIxEZGQk/Pz9cuXIFPj7sXgV1mTx5Mp5++mndz8ePH8fs2bNx4cIFeHp6Ytu2bXj66afh6uqKc+fOYc2aNYiJiTFUdYIgCCbGGG3gfnbIqFGjMGqU/nhJUzHYaA8fPhwXL17Evn37cPnyZdjZ2aGsrAyjR4/G1q1bERYWhtDQUK6yTUdHRzg6Pkg/kslkkEgkuqKdjRs34qWXXoJGo4Gfnx+io6PxxhtvGKo6QRAEk6qqB8ZaTHNdDDbaNXe6Wq0W169fx8WLF3Hx4kVs374d7777LrKzsw2aYjN48GBdYQ0A/PHHH4aqeZ8ZM+qf6VkfnXWqPBuEYy5UIEciAE8CBMunxpHIgqNJ7A9QnqSO8ePZMrBjJJ9zOAm5To4cbgtmi0SAmct9r5B9fOfRl8tDJWO3Y0xldHXs2JGdg80zom74YPbElgIndtzIMydD73XnGqnDjbEtgR0MnP0o26TV/ddvzBQY49NuTkzi07ayskJwcDCCg4Nr5VcXFBSYYnmCIIgWx1j3SHPRJKM9ZcoUxMXFIVRPNWFycjJWrFiBH374AZ6e7Mq5FsHDA7Cv09SGNVmeZ0vEkVvqMIzd75lnwCqrYIyjPxDX4aF7d7aMvYZjwCrr9ePoOiW5zZHvz/PiceziWHn5Haw4crBZuf8A1/MucGGnzbKKPHnisxxvX678ZK68/axi/dd5IrQI45AxHcYa7Z9//hmTJk0yrVJootFuzjQWgiAIMWGsT3v27NnmN9rNmcZCEAQhJoz1aQsCx8xQA2iyT7u50lialZKS+oFIVrMnnqNur15MkevX2cuwWnsD7CMoz5uKp2q5c2e2zI0sdkCzm4ahEKsuHwAmTmTL8MCTWM6KBvP4G3g2Kxxl4Tz9ylkxcE8Xdl9pd3d2H2webJXsXtjMnuZJScwlrKw43CMc/7cOdUoEGvvfMUXKX3MgnoJ6giAIEWGs0VYoFAgMDNT1ZqpZ02IMbcNod+hQPxDJmnLLU2LIEayM6M/hDOPYxXl56Q/q8rRm5egXxdUxdUAkOxBZoAzRe92z+DD7RjxHA558SZ4dMGsyMs/EY55oMMfRy8+BnaIoBOqvtC1VsHfRruUcTbA40iXP5LBT/vqE6v8bFPXXP1gZAKI4MjchZ6fhumlqZ7VJqho+KajVD3bhanbmYz0cHR3x0Ucf4fz58/jzzz+xceNGFBYWol27djojbvYydoIgiNaCsTtta2trTJ48uVYadEZGBpKSkpCUlNRoryUWRo0bW7p0KXpx+HUJgiAsDWONdkOByICAAAQEBGDcuHEG62Ww0d69ezfOnj2L3377TdfoCQBef/11wyfMNBcZGfWDUaxKOg7Xx3nvEUyZiCz9lWAAuCorWSd8npM5jwuFx9sA2W2miCcrv5dn2C7PfwpHYI8rkZj1xDmSmkvk7MCT1ushpoyHFbthlAT6MxM0GrYupTYeTBlXO3aEu4/TFaaMyoYRROQIpHMMIAJSc5kiFZ1r61Khabja1Vij/cUXX+i9fvjw4QZbUbMwODfPwcEB//nPfzBy5Ehcr+Gn27Jli6FLEgRBiIZqn3ZlpWE+7YZ6ZWdnZ+P9999H165dMYwnbawBDDbaEokETz31FP7973/jqaeewq2/Z7M1V24iQRBES1K906654zaEqqoq/PLLLxg9ejSCgoKQkJCABQsWGGwrDXaPVN9w7NixUKvVGD58OBITE5stN9EowsPrlymzEpvnzGEu68/xkaeSsiPr9gp2jxaFQn8DIOdKdibAsWPs43D//kwRQMo+p5Y6tNd73ZVnfBNPaTOHa4lrDBjLXcPRkcsth6PsnieThUOmBG56r3sos9n34fgbVLVjDxm+q2TLODH+lO3dDdjKNgTH36mum7C0kTRzY90jKSkp+PLLL/HNN99AJpNh+vTp+PTTTxEYGAiVSoXY2NimLwojjPbUqVN130+aNAmVlZUYNmwY1IacIwiCIESGsUY7JCQEI0eOxN69e9G7d2+T6WWw0d68eXOtn6dMmYLKykosWLDAaKVMTnZ2/Tzt0aP1P4Zjt8NK9QYAeyU7qCTI2I21mPfi2GHUGQDUIF28OZpB2bADe657ftZ7XZjI7skgucCREmWKvrYAe6fNczLIZQfBSkMfY8rwHAzc7BiTaRzYgd6rt9i53Fr2U+Lqx8V8/7IqlAG+ylYOZepWYDTWV8TYPO1Zs2Zhx44dmD9/PmbOnInnnnvuwTQtIzBpk5CZM2dSO1aCIFoFxvq0t2zZguzsbMycORNbtmyBt7e3rneTxggnOXV2IgiCaABTBCKdnZ0xZ84cHD9+HGfPnkVAQABmz56NTp06GaxX26iIjIoC6o49Y+Xdcvg+eI6xPKXYEl/2Qk5OjCBiYiJzjRCOYbr4gzGQFwB4cksZSbWS+F/Za/AknzP6YANAlYv+oB0AWJ/8U7/A/v1sXR5/nCnC41KT5N5jytxjBP862LBPvIGBbPcIT18vnqlJzK4QJgpMl7izB2lr6pTD65tcY2394HtjCQ0Nxdq1a7FmzRr8+uuvBqdH006bIAiiAar7aWs0pp0RaWNjg0mTJmHv3r2GPd50qhAEQbQeWsW4MYslNBRwda39O1a0muMc61z3nNUQHBkFPPfq5M+QcWcM0gW4zngZPdil+QFgZC4AQGCg/us8Nck8526OjIL02+xlOrH05ckH53DVSO5ytDXgyDDoYMXoYV3Ifm+m57KzlniSHXiyR1j/bnYcGVS35WyZTkq2a8lNUVzrZ3lZw68VGW2CIAgLorLyQeZvq5vGLnpqzg36m0M5+vs9D+nIsZvkGf7L05ebIwhz5XrDTW2q8fVlVzs6cOyaePJyeXaUOHtW/3WOETl3ctiBMp6BMjyTYMpd9Feu8sRw3TiqUtXe7ArZa9fY93pYeUa/AOvkACDEn+2ozcyxZsrwNCJj/at4atg7ZIWCXXkJLfuEUeZfu2lXWSMBTmNnRDYXbcNoEwRBNBGN5kH2CLlHCIIgRE5lJVDdSoncIy2NSgXY1nYv8MS4mPBEaXgCkRwf42F2WfoFFN7s+9i4M0WCnDh8CSdT2TKs4RgcLiFvjqfE4QVg9p4GwPwblCr1u6cAAMXsPGIlRw9rnlzuss599F53duA4z7NcWAD8OGbUCVJ2Hnwq6y3DUfTA4wpDZ/abhrd3HO20CYIgLAjyaZuR/93qAien2rtiVj+oQ8X6W6ECfKlOD/E0ueE5e7GqA+Pj2WvwjIbjOYLwNPeJZKQgcgyM5YEnyOhoxX59swv1Bz19rh9hrlHSaxBTxoHj7dDJi6NpFwsrjgA4TxojRyBdcvIEU8bBv59+AY73A0/RZInGmSnjtuOnWj9bNxIl1Wge2AnaaRMEQYicmnsp8mkTBEGIHNppmxGJpL47hBX32LOHve7q1Rw3l3MMsOVxobBcEjyDcjmCPVdz2IGyEI5qxnvF+t0NHcoZgVUASht2BRxPQ6Pevdn53qxcYx8OVwJPvjKP96mDF9slUaHUPyHqMjvGCJmsC1PGhv1ngk0gw/UBwJv1ltGwE+H7dWY7ljOy2HnlbnUj3GUNu6PIp00QBGFBaDQPUv5op00QBCFyKiuB6tm7Ypqi2CaM9o8/1kvTxtNP63/M+PHsdXmq2O15Ukz++IMtw/LXPPcce43jx5kiIaPZbpZ7lfpzhAG2q0Dand0E3s1KxZQZEMmzBWJ3IO7enZEtZNeducZDSkYTJwC4fZspckP+MFOmm0x/v+w+XdnP+WYh2xUWJGM/p3vlrkwZVitsKyt2tpbHHweYMjmyp5gyNt1rZ/mUljasHO20CYIgLIiqqgdGm3zaLYyvb/25vqyBMjw9kXjSXD3yOSbBhIayZVgf9YcPs9fgCETeq2TvvnieNysPnuf1RSrH0F6OIO7BVPY0k6H9Gbt6jmpHdtkfuCpkbcLZO22uRmQMuKqCOY6Tly+zd9pD++rPPb+Yys6v9uAIgHfkaOxVN9+7sXS+ysoH/zK00yYIghA5NQ01GW2CIAiRQ0bbjDg71z+O//67/sfwNO3hSa/uOnIAU8b2Vgp7IVZDZ556bo7zcId2HM47ruY++hsspXN4PmSybkyZToXnOdZhu0cOHtOfyz00iu0TykN7pkx7B7a+nRyymTJQMvxLHG4NB3c/9n1OXmaKFBez+1wXKPW7P3ji9ZCzG7S1Rx5TJq249t+ptJFYa00/trE+7aSkJMydOxdpaWnQarUIDQ3FmjVrMHDgwCav1SaMNkEQRFOpOY3dWKMdFBSEnTt3IvDvtpS7du3CqFGjkJubC0dHduZMTWgaO0EQRANUz4isOSvSUDw9PREUFASJRAJBEGBtbQ2FQoEcnjLaOrSJnfb69Q9Sd6phpTXzzJ3lKaEe587uDsfVFJqVssHzruLprMcjw5ElcTjpEb3XedqMT5/OlintGsGU8efwHHVnpGGrKvWXjQN8Xeiyvdn6+ijT2AsxULVjuz6cr//FlCnoNZQpM5DtLWN6a3jckVwvMIdbKKtOaX4jVezQaGoW17BrBnhwd3eHQqFAVVUVXnjhBXTqxK5XqEubMNoEQRBNparqgdEuKPi4UTm1Wo0qPf4Te3t7SP7eNRYXF6OiogI///wzlDxxqAZoE0a7Y8cHvqlqWJ/sPENpeAKRpZHsHsuuThwOM9YxiieSc/06U6TKhT2FxJrjGPLkk/qv193tNERQIHvizI1U9g6YZwIOK6+cR1+ejSDPadind0emTFm5/uddzpFW3p5je8vznNxsOPp/5zDqFew4jrYc/5QX09l1Bk/436n1s7yRSGTNaezOzgtQXLymQbkJEyZg7969jd4vLS0NHTt21P3s6OiI6dOnIywsDMHBwejfvz9T55qQT5sgCKIBavqzq6oazy7as2cPBEFo9Kumwa6JWq3GjRs3mqwXGW2CIIgGMGUgcs+ePbh06RI0Gg3Ky8uxevVq3L17l1L+GsPGpr57hBXk4hkiOnEiWyY5mS0THs7uAezISGwu48i5dWYlpwNIxUNMGZksgCnjwAhO8bgbMu6yXR88E0V4AsY9eui/ztOunMdNUFjILtfmeVLODP+HM89IOI7mVXYDOQJl6YyeEAAzkC54sXO9JdeuMmUqOdow1PNRNRKJVKsfJDAIHLOh9ZGfn4833ngDmZmZcHBwwMMPP4y9e/eiSxd2T/O6WMxOW61W47XXXoNMJoNMJkNsbCw0YipTIgiiVWHKnfasWbOQkpIChUKB/Px8HDp0CEOGDDFoLYvZaa9cuRLHjh3DlStXAAAjRozA6tWrsXTpUuZj58wB6uavBwfrfwzHppQrbY1nx871hmDkpJ3lmFQyiDUcGHwngwkj2elPVTb6Kwx5Uip5gsEB2jtMGVVXdkWkvUb/Llllw7FDzslnikilHOskJLBlOnfWf53naMDKcwTgaMcRJD99mi3DiExL8tmVjDydyrrzVFYq6lQXNxKIrNma1didtimxGKO9ZcsWrF+/Hj4+PgCAJUuWYOHChY0abZVKhZKSEgCAUln/KMnq78vTK7ux/M6mrsPSBQAEtf6+xmVl+svGAUAOdg52OUfagVxuvNHmee14XhdJYzXINVA5sRdiG2228bLn0KVUy/H68rxpWPn0dRvINwRPyhnPH6Gigi3Dem1Y6TucyG3Zo+UkZbV1kf/9Wgp1LLNGU/O5c7wOLYVgARQWFgoAhBs3buh+d/36dQGAUFxc3OBjli1bJgCgL/qiL/ri+rp586YgCIJQUVEheHt717vu7e0tVFRUtIjN04dEEMS08W+YjIwMBAYGIi8vD+3+PiLl5eXBy8sLGRkZ8G+gmZJKpUJFRQUKCwtha2uLwMBAZGRkQMpz5jYzcrkcAQEBpG8zQfo2L5amb0lJCQIDA1FUVAT3v5uqKZVKVNYJCNvZ2cGBqxF882IR7hGXv4sASkpKdEa72vXh6tpwA3Z7e3vY29vD3d0dKpUKy5YtQ/v27WFfdxqCCLG3tyd9mxHSt3mxVH1rNm5ycHAQhYFuCIvYaQNAQEAANmzYgEmTJgEAduzYgQULFiCdp8cnQRBEK8FiUv5mz56NVatWIScnBzk5OVi9ejXmzJljbrUIgiBaFItwjwBAXFwcCgoKEBISAgCYNm0a3nnnHTNrRRAE0bJYjHuEIAiCsCD3CEEQBEFGmyAIwqIgo00QBGFBkNEmCIKwIMhoEwRBWBBktAmCICwIMtoEQRAWBBltgiAIC8IijLZKpcLcuXPRqVMnuLq6Ijg4GFu2bDG3WgRBEC2ORZSxazQa+Pj44Pfff0fnzp1x6tQpjBgxAv7+/niSMRGDIAiiNWGxZewTJ05Ejx498N5775lbFYIgiBbDInbadVEqlTh9+jSmTp3aqEzNIQienp6QSNiTvQmCaHsIgoDS0lL4+vrCykRjz5oTi9tpC4KAF154AZmZmTh48GCjL/I///lPLF++vIW1IwjCUmlsCpbYsCijLQgCXn31VZw7dw6///473NzcGpVVqVTIzc21qDFjBEG0PNXj0YqLi/XaFLFgMe4RQRAQExOD06dP4+DBg8wX197eXicjlUrJaBMEoRdLcaFajNF+7bXX8OeffyIxMREeHh7mVocgCMIsiN/rDuDOnTv49NNPkZKSgqCgILi4uMDFxQWvvPKKuVUjCIJoUSxipx0UFAQLcr0TBEE0Gxax0yYIgiDuQ0abIAjCgiCjTRAEYUGQ0SYIgrAgyGgTBEFYEGS0CYIgLAgy2gRBEBYEGW2CIAgLgow2QRCEBUFGmyAIwoIgo00QBGFBkNEmCIKwIMhoEwRBWBBktAmCICwIMtoEQRAWhEX00ybqk12gxM2MUnQJcMWpo/tx+fJlAIC7uzumTp0KDw8PixmfRBAEP612p61SqSCXy82thsnJz8/HzOh/olPXh/H06IkYHH0Cew9fRUFBAQoKCnD69GmoVCqcPn0anTt3xoIFC3DmzBlzq00QhIkwehr7iRMnEB8fjwsXLqCoqAgeHh7o1asXRo8ejSeeeMJUejaZf/7zn1i+fDkAoKSkpFUM9q2oqEDHjp1RbtsN7sFT4PHQs9CU50NbkY1Dm/rBx9NBJ6vRaHDmzBns3LkT//3vf7F48WL84x//MKP2BCFO5HI53NzcLMZOGGy0ExMTsXDhQpSUlGDIkCEIDQ2FVCqFXC5HcnIyDh06BKlUinXr1iEqKsrUejNRqVTIy8tDQECAxfwxGmP37t04f/48li5dioQ/0xDzSRqcfR6Dla0ztOoylGWfwn+XReCxsAcDj7MLlLiTXYEgH0d4ulpBpVKhpKQE48ePx5o1azBs2DAzPiOCEA+WZrQN9mmvWbMGGzZswMCBAxuVOXr0KFavXm0Wo21vb28RfwB9qNVqvPHGG/j555/xxRdfAAAeDvaBleQ2KuUZsJMGoFKeAWsrCQK9HXWP256YhXc3p0ArCLCSSLDy5YfwbJQvnJ2dMX/+fLzwwguIiorCW0tWoULrhiAfx1q7dIIgxIvR7hExY2mfoHVZsGABkpKSsG3bNnh5eel+vz0xC3FfpKBKK8DaSoIVL903ysD9Hfbg6BOwdvLRGfW67pPi4mI8P2s+khV9YOfRHVYSK6x6JVi3BkG0JSzNTjRr9khubm4tY0PwcTklA/eKBbwybzE6+XnA1ta21vVno3zRv6cM6TkVCPSuvUu+k10BrSDAURoAK1tn2EkDUFaWhfScCp1cRZUD0uxnwMXDB/K0vZCn7cc76tfQv+dTtOMmCJFjVPbI/PnzG72Wl5dnFreIpbNl12U88ugATHl1HcYsuoJfjuY1KOfj6YDHwjzqGdkgH0dYSST3d9jqsgbdJ9WG3U4aAM+er8DWxRe3D7yE3fsONetzIwjCeIwy2vHx8VixYkW93+fn5yMqKgohISHGLN/muHE7D9FznoGTT1/49F8FaycfxH2RguwCJfcaPp4OWPnyQ9BWZKMs+xS0FdlY8dJDtYx7TcMuAdDh0bfRvudcfPvlR2jF3jKCaBUY5R5JSEjAgAED4OnpiejoaABAQUEBoqKi0L17d/z4448mUbKt8Mm/PoWtkw8Ch38BazsXSBpwbfCgz30CPDDscV+koKwsC9ZWEnyyKhrPDFkBpVKJDz/8EG+99RYcHR0buQNBmJ+aGVJtya1nlNHu0qUL9u/fj6ioKLi7u2PEiBEYOnQoOnfujJ9++gnW1tam0rPVo1Ao8PbbbyIhvR/UiixIrKwbdG3w4uPpoPeN3JhhV6vVOHPmDAYNGoRdu3bBz8/P4OdEEM1FYxlSbQGjKyLDw8MRHx+P6Oho9O3bF0FBQdixYwdsbKhCnpdt27Zh6NCh8G3niFXRPfS6NkxJQ35xqVSKX3/9FcOGDUOfPn1w+/btZrk3QRhKdoES725OgbWTD5x9HjPIjWjJGGVZ//Wvf+m+HzRoEI4cOYK5c+fi008/1f1+3rx5xtyi1XPz5k288soriI+Ph0QiYbo2WgJra2usXr0aw4cPR2BgIEpLS+Hq6triehBEQ/BkSLVmjMrTHjJkiP7FJRIkJiYaurzRiD3/UqPRoG/fvpg4cSLeeecdc6vTIIIgoG/fvhgxYgSWLVtGTagIs8NTi9AUxG4n6kLFNWbm4MGDGDJkCKysxNu7686dOxg7dixCQkLw9ddfU4CSMDv6CsyaiiXYiZqQ0W5hqiPeJfeuQlGcjUmTJplbJb1U6+vposEb817ErFmzMHHixAZl2loUnzAv2QVKk7gRxWgn9GGSaGFmZibi4uJw7tw5lJaW1rp269YtU9yiVVAd8a6qUiPj91cwc/bLELPNrhuhX/GPzzBxqB8OHDgALy8vREREtOkofmvD0j58WRlSrRWTGO3p06fDyckJixYtgrOzsymWbHXUjHiXpu6Ctb0HjmT1RnaBUpRvvJr6Ov7tN1z6n+sY0MsT9+7dw/PPP491/9qMD/a0ryUT90UK+veUifI5WSo8xtRYg0sfvpaDSYz2uXPnkJ+fDzs7O1Ms1yqpjng7uPii6Op3CHzqK2jVZaKNeOuL0M+YMQNBQUGYMPEZWAdOht/AD9pkFL8l4DGmxhrchj6g29KHb06hZaUKmiT6FRYWhuzsbFMs1WqpLh1XK7LQ/fljsLZzM7hwpiVg9TAZNGgQ9h44AkeP7lCVpKNKWWxUMRBRG7VajWu3cvD2xiRYO/nA1sUPVRoVFn18CGcupECj0UCj0eB2VglXznJ2gRInLxc1mMtcsxdN9YdvlVZAek5FPVl965hapiXYnpiFkQtOm1WHpmKSnfbEiRMxZswYxMbGokOHDrWujR071hS3gFqtxvz58/H9998DAKZNm4b169dbTBGPj6cDnut9D//67EN49X5TF/EW606moVL3uvr26x2Kde+64/W4/6Dw+g74PfEe3p/3mGifU0vDclkcPXoUly9fRlpaGtLS0rBlyxZcuHABo0ePhlwuh7W1NVw6j0PHEd/gzv5ZUNw9CqFKheF/WiH5yl9IS0tD//79IbGyg7WDB2Rhs+DTLw53z63DvHk/ILJHJ/j5+UHi2Q8rtt6BVquBtY19vZ14zQ/oxnq0A6bb9YvFFaM7YTh6t/i9jcEk2SOdOnVqeHGJxGSByGXLluHXX3/Fb7/9BgAYMWIEJk6ciKVLlzb6GDFFhVUqFcLDw/GPN97BI0+MNVvhTFPhidBn3FNgwfz5OH38d+zbuwdhYWEtrKX4qGmYoKnA6B4Z0JZcRVJSEh577DGsXbsWCxcuxK3bd+Hm6Y+Hw7rh1bkzIAgCCgsL0a5dOxSVgZmPnJ5TiiEv/w6JjSPs3DoCAiC/uQszBmkgL7qHW7czcN1mNjSV5biz/wXYOHWArZMXli+eizcXxODYsWOwtbXF5UwnfPhTHrQCGkyh48mNNpVMS3HychFeeO88HGQhuLzZVxR2ggeTbFPT0tJMsYxetmzZgvXr18PHxwcAsGTJEixcuFCv0RYTGzduhI+PD16dO8OiClR4IvQBHVyw/fv/YNOmTXjxxRdx4sQJi3qOpub0+RS89tZmKItuwPeJFdBUFOCLzV/gpReexltvvYXIyEgAwGMjF2D35hRoiwQc/1OCgB5FeDbKF05OTgAAHwcwTzuB3q5Y84/HEPdFCtSK+zLr/zlTZ3CrDZN70HC4dZNDVZAM+e0DcPfqAgDYsWMHEhISkJaWBmtrG+xKuAQ7FGPvLxtQeKMLunTpgh49euBOvj2zCpGnUlFM1Yy6E0ZpZove11gM9mlPmTIFycnJemWSk5MxZcoUQ2+ho6ioCHfv3kWvXr10v+vVqxfS09NRUlLS4GPENo29sLAQa9eubdXGLCYmBkeOHEFVVRU+//xzqNVqc6vUIlRWVuLixYsAgJUrVyJqYB+U5ZyFS8Ag2Lj4wtm3L3wHrcXzs9/EmDFj4Ofnx90/49koXxza1A//XRaBQ5v6NehG0CdTtw2vla0rpP5PYPSIwQCADRs2IDk5GWVlZUhJuYZh/TrCz8sVdnZ2OHz4MJYsWYKtW7ciyMcROceXIXXHU0hPmIvs4+/BClXwcFYjMzMTGo2Gq5c7jwzQMj5vXRtjZU6z3aM5MNg98uOPP2LJkiXw9PREVFQUgoODdYN9r127hsTERBQUFGD16tWYPHmyUUpmZGQgMDAQeXl5aNeuHYD7Qxa8vLyQkZEBf3//eo8R0zT28vJy3e6pLVBQUIAJEyagsrIS33//PTp37mxulZqF3bt344cffsCePXsQEhKCkydPori4GHKlNZ58/ZxeF0D1Dpg1oNkUmKp6cO1/fsf6rUehKs2Cpvwevtj8GewVZzFjxgwoFAr4+PhgxDMxOJrTF3lX/guJVo0xg7siqm8nTJs2DXK5HHl5efjjLyU++D4DWokdbKyt6+nTVJ93VVUVysvLoVQqUV5eDgcHB3To0AEnT57E3bt3IZfLIZfL8fLLLyMjIwMff/yx7ndRUVEYPXE6HurUwex2ghejfNqCIGDfvn2Ij4/HhQsXUFRUBA8PD/Tq1QtjxozByJEjTVKeXVRUBJlMhtTUVHTpcv9Yl5qaim7duqG4uBhubm71HiOWaeyZmZno3bs3rl271qCerZWqqiqsXr0a69evx8GDBxEREWFulYymoqIC+/fvh0ajQf+oMXj99dcR3K0jZs94Hh07dqwlyzKULe3bNVX1YGPrlJaWIjMzEy4uLrB2bIeP1/8bZSXZUJWXoKSkBNu3b8fu3bvx0ksvoaSkBEqlEi++8iZWrHwPE0cPwe3bt+Ho6AhrGztIev4bKvld3Dv7IQStGtBqsGjBy3hv2dsYO3Yszp49C6VSCZVKhczMTJw6dQpPP/00HBwc4OjoiFdffRWrVq1CbGwsUlNT4erqCqlUirVr16K0tBTbtm2DVCqFq6srHnroIXTp0kU0sS8eLKaMPSAgABs2bNCVfe/YsQMLFixAenp6o48RQyBy7ty5sLe3x8aNG81yf3Nz5swZ9OzZE6mpqbC1tUW3bt0MXstcFXtyuRyvvvoqdu/ejZCQEPQePAW/3+7J3AmyDKUp+2dYGlqtFlqtFjY2NsjJyYFCoUB5eTnOJudh9c8CHGTBUJdlQ9BUoCL/MjYuHoBxwx7GzZs3UVVVBQcHBzg4OOhO3hKJxGDXoxjsRFOwGKO9dOlS7NmzB/v27QMAjBw5EuPHjxd19sjVq1fRr18/pKSk1EuFbGt8/fXXmD9/Pt58800sWLCgyU2nWjJNTKPR4PDhw/jxxx91WUqffvopRo0aBXtXb5PukE21A24tmCO7xNx2oqmIt7VcHeLi4tCvXz+EhIQgJCQEjz/+uGjbmVZTWVmJDRs2tHmDDQCzZ8/GyZMnceTIEYSHhzcpSNkSTe+1Wi0EQcCpU6fg5+eH2NhYBAYG4pFHHoFEIkFMTAw6duzYpEIUHhob0NxW4Zlx2taxjMoUALa2tti0aRM2bdpkblW4KCgoQGhoKHr27GluVURDcHAwEhIScPPmTdja2uKjjz6Ct7c3nnvuOdjb2zf6uOZKE6uqqsLJkyfx66+/4scff8TOnTsRGhqKhIQEtPfrjvQcJexda58IeAtRCMMRwyAQMWMxO21LIyYmBuvXrze3GqKkOpjcvXt3fPbZZwgICMCSJUtQVVXVoDxvmhgPCoUCf/zxBwBg1apVmDFjBtRqNbZt24ZHHnkErq6uuF7QHkNiTuKF985jcPQJbE/M0j2edoItA51AGsdifNqGYC5f1fXr19GnTx/cunULnp6eLXZfSyUpKQkJCQlYvHgxvvvuO2RmZmLs2LEIDg7WyfAG7eoGKwVBgEQiwccff4zdu3fj1KlT6NOnDxITE1FVVQVbW9taASxenyr5oi0DnuC1pfm0jXaPnDhxosGUv9GjR+OJJ54whY4WxwcffIC5c+eSweYkMjJSVyXo5+eHhIQEfPDBB3BycsLBgwfRoUMH2JWewSdz20MluOLhkAB08nMHcN8XXVlZiXv37uF4CvDuxrMoub0flcU34WaVifFjnsS///1vODs7IyYmBtu2bUP79u0B3J+FWRdeV0xb7eVsSYilx4mpMdhoJyYmYuHChSgpKcGQIUMwfPhwXXFNcnIyZsyYAalUinXr1iEqKsqUOouePn36YNy4ceZWQ1TwpusNHjwYgwcPhkajQXJyMoKCgpCWlob169cjOzsbubm5GDduHLZu3YpRo0bpsok8PGRw7/dvWDl6AYIWzn4DYOXcDvPeeB4A8PLLL3PpST7r1kFrbjdrsNFes2YNNmzYgIEDBzYqc/ToUaxevbpNGe3MzEy88sor5lZDVBiy47GxsUF4eDiA+wHMw4cP665ptVoA93P1JRIJbGxscPZaqa7CUNpxmK7CsLCs/ltc3wcIT3dDQvyIqceJqTHYaCckJDBlBgwYoOvK1xa4d+8eQkJCcP36dXh7W1a7x+aiOXY81VW2NXO9TdlelLIXLJ/WfGKi7BETsn79ekycOJEMdg1MndfcGDxZHU3J96bsBcumNWf5mDRPWy6XY9OmTbCxscGLL74ImUwGAFi3bh3eeOMNU95KdBQVFeHzzz/HyZMnza2KqODd8ZiiRJ21Q27NR2aiPq31xGTSnfa0adMgk8kgk8nw5JNP4tSpUwCAvXv3mvI2okStVuPDDz+slaZG8O14tidmYXD0iQbzog25X2M7ZFPmexOWQWs8MZl0p61QKHRR+vHjx+O5557DvHnzTHkLUVJWVgaJRIKXXnrJ3KqIEn07npaM8lOQkWgNmNRoq1QqKJVKODg4wNPTE3v27MHzzz+PCxcumPI2ouPzzz/HsWPH8OmXP5ilC50l0Fhec0u7LFrrkZloO5jEaF+7dg3BwcH4+OOPUVxcrAvEOTo6YufOnfjhhx9McRtRolQqsW7dOkS//RkGR59odYn8zY05ovxUGENYMibxaT/77LMoLy9H3759a2VO3J87Z43p06eb4jai5Ouvv0anzt3w7TFps3aha6205ig/QTQHJjHaU6dOxaxZs2r97vjx43j88cdNsbyoCQsLw/+LiWuRtLbWCs8cRIIg7mMSo/3222+joqICH3/8MYD78yPHjRuHzZs3m2J50ZKWlobHH38cI5/sT1kJRtIao/wE0RwYbLTj4+ORlpam+/mbb77BZ599hjlz5mDhwoU4cOAAxo4daxIlxUhVVRVGjBiBAwcO0BGfIIgWw+BA5IoVK3DlyhXY2tqiR48eCA8PR//+/bFv3z788ccf6Nq1qyn1bDIqlQpyubzZ1t+5cydsbW0xYsQIAJSVQBBEy2DwTvvMmTMoLS3FiRMnEBMTA1dXV2RlZUGr1SIkJARhYWGYNm2aKXVtEu+//z4CAgKaZW1BELBq1Sq88847tabN0xGfIIjmplmGIOTk5ODChQu4ePEiFi1aZOrluVCpVMjLy0NAQIDJm5trNBp89dVXePHFF2FjYzET2wiCaABLG4JAk2s4qe6NEejtgOLcNISGhppIS4IgzImlGW2D3SNTpkxBcnKyXpnk5GRMmTLF0FuIhpq9MR579nMMHDwMGo3G3GoRBNEGMfhsP27cOIwZMwaenp6IiopCcHCwbnLNtWvXkJiYiIKCAqxevdqU+rY4dXtjZB97F87+k5BXooGPJ7lGCIJoWQy2Os8//zwmT56Mffv2IT4+Hp999lmtGZFLly7FyJEjawXqLJGavTEq8q9AVZyKDn3fpXaeBEGYBaO2ijt37sSkSZMwatQoU+kjOmr2xrC2c4XPE6tga+dIhTMEQZgFo7bBs2fPNpUeoqW6cEZVcBHKwmtw8uxKhTMEQZgNo4x2K048qcWzUb4Is9qGYQGnqTcGQRBmxSj3iEQiMZUeoubKlSs4eiQRt259CQ8P2mETBGE+jDLaCoUCgYGBiIyMREREBCIjIxEZGQk/Pz9T6ScKVqxYgdjYWHh4eJhbFYIg2jhGGW1HR0d89NFHOH/+PP78809s3LgRhYWFaNeunc6IW3rKHwDMnDkTjz32mLnVIAiCMM5oW1tbY/LkyZg8ebLudxkZGUhKSkJSUhLOnz9vtILm5tixY3jyySdhbW1tblUIgiCMK2N3dXVFaWmpKfUxKcaWp169ehX9+vXDrVu3IJPJmkFDgiDMTZspYweAL774Qu/1w4cPG7O82XnvvfcQExNDBpsgCNFglHukob4i2dnZ2Lp1K7766ivcvn3bYnt0JCUlISEhATdu3DC3KgRBEDpMUmNeVVWFX375BaNHj0ZQUBASEhKwYMECi87jDg4Oxt69e2mXTRCEqDDKaKekpODNN9+Er68v3n77bfTr1w+pqak4dOgQXnzxRVPp2OKcOnUKN2/eRN++fc2tCkEQRC2MMtohISG4evUq9u7di6tXr2LJkiUIDAw0lW469u7di4EDB8LDwwNeXl545plncPfuXZPfB7h/apg7dy4uXrzYLOsTBEEYg1FGe9asWfjjjz8wf/58fPnll802k7GkpASLFi1CRkYG0tLSIJVK8dxzzzXLvb799ltYW1tj6tSpzbI+QRCEMRg9uaasrAw//PADtmzZggsXLmDMmDGYOXMmBg0aBKlUiqqqKlPpquPSpUuIiIiASqXSO+6rKak82QVK3LqrwJSxvbHlqy8xbNgwU6tNEIQIsbSUP6O7+Ds7O2POnDmYM2cOkpOTsWXLFsyePbtZg5BHjhxBSEiIXoPdlGns2xOz8O7mFGgFATYPr0GRFY0SIwhCnJh0QkFoaCjWrl2LzMxMfPbZZ3j66adNuTwA4Pz584iLi8P69ev1yvFOY6+eTKNRK6HIOAyH9r0Q90UKsguUJtKYIAjCdDTLWBkbGxtMmjQJe/fuNejx3333HVxcXODi4oKwsDDd7//66y88/fTT2LhxI4YPH653jbfffhsZGRnMe93JrkCVVovcs2sBiTXspAGo0gpIz6kwSHeCIIjmRJSzwKZNmwaFQgGFQoErV64AAC5fvoxhw4ZhzZo1mD59OnMNe3t7Lv9UkI8jFLf3o7IkDe0jXrs/ocZKQpNpCIIQJRYxmfbKlSsYOnQoVqxYYfJpOd4ye/jZXID9owtRkXcR1lYSmkxDEIRoMTp7pCWYPXs2/u///g9OTk61fp+cnKw3L5wVFdZoNFAqlXB2dkZOoQrpORUI9HYkg00QbQhLyx6xCKNtKKw/xuLFi5GTk4OtW7e2vHIEQYgCSzPaFuEeaQ527tyJrVu34ty5c+ZWhSAIgps2abRTUlLw4osvIj4+vtWNRiMIonXT5oy2UqlE9+7dcfz4cYSEhJhbHYIgiCYhypS/5mLbtm0IDw9HZWUlGWyCICySNrHTzs3NRUxMDP73v/9h165dsLe3N7dKBEEQBtEmjPaRP8+hXG2HQ3+eR0gXH3OrQxAEYTBtwj3y/q/OuKSdhvHvXMP2xCxzq0MQBGEwbcJoWzt6w9nnMVg7+VAzKIIgLJo2YbTtXP1gZetMzaAIgrB42oTRrizNhFZdRs2gCIKweNpEIFKrzEFZtpyaQREEYfG0CaO9d92jKCqzpWZQBEFYPG3CaHvLHNC9o/gbwRAEQbBoEz5tgiCI1gIZbYIgCAuCjDZBEIQF0ap92tXzHeRyuZk1IQhCrFTbB0uZB9OqjXZpaSkAICAgwMyaEAQhdgoKCuDm5mZuNZi06nFjWq0WWVlZEAQBgYGByMjIsIhxQnK5HAEBAaRvM0H6Ni+Wpm9JSQkCAwNRVFQEd3d3c6vDpFXvtK2srODv7w+VSoVly5ahffv2FtGW1d7envRtRkjf5sVS9XV0tIxK6Va90yYIgmhtUPYIQRCEBUFGmyAIwoIgo00QBGFBkNEmCIKwIFq90Var1Xjttdcgk8kgk8kQGxsLjUZjbrUAABs3bkTv3r1hb2+P8ePH17omRr1VKhXmzp2LTp06wdXVFcHBwdiyZYvuuhh1jo2NRUBAAKRSKfz8/PD666+jsrJStPoCQEVFBbp27Vor/UyMus6aNQt2dnZwcXHRfZ04cUJ3XYw6A0B8fDx69eoFZ2dn+Pr64vPPPwcgXn3rIbRyli5dKvTs2VPIysoSsrKyhJ49ewrLly83t1qCIAjCzz//LOzatUuIiYkRxo0bV+uaGPVWKBRCXFyckJqaKmi1WuHEiROCu7u7cODAAdHqnJycLCgUCkEQBCE3N1cYPHiwsGLFCkEQxKmvIAjCwoULhUGDBglubm6634lR15kzZwr/+Mc/Gr0uRp1/++03wc/PTzh06JCg0WiEwsJC4erVq6LVtyFavdH29/cXtm/frvt527ZtQmBgoBk1qs+yZcvqGW1L0FsQBGHChAlCXFycIAji1zk3N1eIiooSZsyYIQiCOPU9d+6cEBoaKuzfv7+W0RajriyjLUade/fuLWzevLnBa2LUtyFatXukqKgId+/eRa9evXS/69WrF9LT01FSUmI+xRhYit5KpRKnT59GeHi4qHVes2YNXF1d4eXlhYsXLyI2NlaU+mo0GsydOxebNm2qVZQiRl2r+eabbyCTyRAWFoZ169ZBq9UCEKfOZWVlOHfuHORyOYKDg+Ht7Y3JkycjJydHlPo2Rqs22gqFAgBq+Qarv6/uSyJGLEFvQRAwZ84cdOvWDRMnThS1zosXL0ZpaSmSk5PxyiuvwNvbW5T6rlu3DuHh4Rg8eHCt34tRVwCYN28eUlJSkJeXh6+++gqffPIJPvnkEwDi1LmoqAiCIODbb7/FgQMHkJqaCltbW7zwwgui1LcxWrXRdnFxAYBan5TV37u6uppFJx7ErrcgCHj11VeRkpKCX375BVZWVqLXGQBCQkLQs2dPzJo1S3T63rx5E5s2bcLatWvrXRObrtVERkaiffv2sLa2Rt++fbF48WL89NNPAMSpc7VO8+bNQ1BQEFxcXLB8+XIcPHgQVlZWtXSs+b1Y3r/VtGqj7eHhAX9/f1y4cEH3uwsXLiAgIEDU3bzErLcgCIiJicHp06eRkJCg00fMOtdErVbjxo0botP36NGjyMvLQ1hYGLy9vTFx4kTI5XJ4e3vjxo0botK1MaoNHyDO94O7uzsCAwMhkUjqXXNzcxOdvo1iXpd68xMXFydEREQI2dnZQnZ2thARESGaiLBarRYqKiqEJUuWCGPGjBEqKioElUolCIJ49Y6OjhbCw8OF/Pz8etfEpnNpaamwZcsWoaioSNBqtcKlS5eEkJAQYe7cuaLTt7y8XKdHdna28PPPPwtSqVTIzs4WKisrRaVrNT/99JNQUlIiaLVa4cyZM0JQUJDw4Ycf6q6LUeeVK1cKPXv2FO7evSuUl5cLM2bMEIYNGyZafRui1RvtyspKITo6WnB3dxfc3d2FmJgYQa1Wm1stQRDuZ40AqPU1aNAgQRDEqfft27cFAIK9vb3g7Oys+3r55ZdFqbNCoRCGDRsmyGQywdnZWejUqZOwcOFCoaysTJT61uTQoUO1skfEqOuAAQMENzc3wdnZWejevbvwwQcfCFVVVbrrYtRZo9EICxYsEDw9PQVPT0/hmWeeEbKzs0Wrb0NQlz+CIAgLolX7tAmCIFobZLQJgiAsCDLaBEEQFgQZbaLVsWbNGixatMjk63733XeYPn26ydcliKZAgUjCYujYsSM2bNhQryNiTUpKStCtWzckJyejXbt2Jr2/VqvFQw89hG3btiEiIsKkaxMEL7TTJloV3377LQYOHGhygw3cLx6ZNm0aPv30U5OvTRC8kNEmLIJnn30W6enpmDJlClxcXPDKK680KBcfH4+oqCjdz7dv34ZEIsHXX3+Nzp07w8XFBW+++Says7MxfPhwSKVSDBo0CDk5OQDuV3wuWrQI3t7ekEql6N69O/bs2aNbb+jQodi9e3fzPlmC0IdZs8QJogkEBQUJu3bt0ivTvn174eDBg7qf09LSBADC1KlTBYVCIfz111+CnZ2d0K9fP+HSpUtCRUWFEBUVJcTGxgqCIAgHDhwQ/P39hczMTEEQBOHOnTtCSkqKbr38/HwBgJCVlWX6J0gQHNBOm2hVFBUVQSqV1vt9XFwcnJ2d0aNHD/Ts2RMDBw7Eww8/DAcHB0yaNAlJSUkAAFtbWyiVSly5cgVqtRqBgYHo3r27bp3qtYuKilrmCRFEHchoE60KDw8PyOXyer/39vbWfe/k5FTv5+rWnEOGDMHy5csRFxeHdu3aYdKkSUhLS9PJVq/t4eHRXE+BIPRCRpuwGGp2kWuMXr164dq1a0bdJzo6GidPnkR6ejrs7e0xb9483bXk5GR06NABPj4+Rt2DIAyFjDZhMXTo0AE3b97UKzNmzBgcOnTI4HucOXMGx48fR2VlJRwdHeHs7AwbGxvd9cTERIwaNcrg9QnCWMhoExbDO++8g40bN8LDwwPR0dENyrzwwgs4cuQICgoKDLqHXC5HdHQ0PD094e3tjaysLN00Fq1Wi++++w4xMTEGPweCMBYqriFaHe+//z6Ki4vxwQcfmHTd77//Hnv37sV3331n0nUJoimQ0SYIgrAgyD1CEARhQZDRJgiCsCDIaBMEQVgQZLQJgiAsCDLaBEEQFgQZbYIgCAuCjDZBEIQFQUabIAjCgiCjTRAEYUGQ0SYIgrAgyGgTBEFYEP8fPbWXLWZ31CwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 375x375 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs=plt.subplots(figsize=[3.75,3.75],nrows=2,ncols=1,sharex=True)\n",
    "\n",
    "plt.sca(axs[0])\n",
    "axs[0].set_position([boxleftspace,0.15+boxheight+boxspacing,boxwidth,boxheight])\n",
    "axs[0].tick_params(axis='both',direction='out',top=0,right=0,bottom=1,labelsize=9,length=1.5)\n",
    "\n",
    "plt.pcolor(data1.time,data1.z,data1.dT2D, cmap='bwr',linewidth=0)\n",
    "plt.clim([-3.5,3.5])\n",
    "plt.ylim([0,90])\n",
    "plt.yticks([0,45,90])\n",
    "plt.ylabel('$z$ ($\\mu$m)',fontsize=9,position=(0,0.5))\n",
    "ax_cb=fig.add_axes([boxleftspace+boxwidth+cb_spacing,0.15+boxheight+boxspacing,cb_width,boxheight])\n",
    "ax_cb.tick_params(axis='both',direction='in',right=1,labelsize=9,left=0,length=1)\n",
    "cb=plt.colorbar(cax=ax_cb,orientation='vertical')\n",
    "cb.set_ticks([-3,3])\n",
    "ax_cb.set_ylabel('$\\Delta T$ (nK)',fontsize=9,labelpad=-10)\n",
    "\n",
    "plt.sca(axs[1])\n",
    "\n",
    "plt.plot(data1.time, data1.dT_k2,'o',color=colorstyle['blue_edge'],\n",
    "         markeredgecolor=colorstyle['blue_edge'],markerfacecolor=colorstyle['blue_face'],\n",
    "        markersize=3,mew=1,alpha=1)\n",
    "\n",
    "plt.plot(data1.x_fit,data1.y_fit,linewidth=0.75,color='k',linestyle='--')\n",
    "\n",
    "plt.xlim([-0.5,60])\n",
    "axs[1].tick_params(axis='both',direction='out',top=0,right=0,labelsize=9,length=1.5)\n",
    "axs[1].set_position([boxleftspace,0.15,boxwidth,boxheight])\n",
    "axs[1].set_xlabel('t (ms)',fontsize=9,labelpad=1)\n",
    "plt.ylabel(r'${\\Delta T}(k_2)$ (nK)',fontsize=9,position=(0,0.5))\n",
    "\n",
    "plt.ylim([-3,2])\n",
    "plt.yticks([-2,-1,0,1,2])\n",
    "axs[1].set_yticklabels(['-2','','0','','2'])\n",
    "\n",
    "\n",
    "fig.savefig('FigS5AB.pdf',dpi=300)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# speed diffusivity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2nd mode speed diffusivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "k_fudge=1.0\n",
    "\n",
    "ETilde_2nd=0.428\n",
    "TTilde_2nd=0.12792\n",
    "\n",
    "TTilde_2nd_err=0.00456\n",
    "\n",
    "c2_2nd=0.0923\n",
    "c2_2nd_err=0.0923*0.028\n",
    "\n",
    "D2_2nd=2.506\n",
    "D2_2nd_err=0.283"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all=pd.read_pickle('FigS4CD_1.pkl')\n",
    "df_Resp=pd.read_pickle('FigS4CD_2.pkl')\n",
    "df_LH=pd.read_pickle('FigS4CD_3.pkl')\n",
    "df_LH_Bin=pd.read_pickle('FigS4CD_4.pkl')\n",
    "# df_RhoS_error=pd.read_pickle('rhoS_error.pkl')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "boxwidth=0.71\n",
    "boxheight=0.3\n",
    "boxspacing=0.05\n",
    "boxleftspace=0.25\n",
    "\n",
    "cb_width=0.015\n",
    "cb_spacing=0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig,axs=plt.subplots(figsize=[2.5,3.75],nrows=2,ncols=1,sharex=True)\n",
    "\n",
    "# axs[0].set_position([boxleftspace,0.15+boxheight+boxspacing,boxwidth,boxheight])\n",
    "# axs[0].tick_params(axis='both',direction='out',top=0,right=0,bottom=1,labelsize=9,length=1.5)\n",
    "\n",
    "# axs[1].tick_params(axis='both',direction='out',top=0,right=0,labelsize=9,length=1.5)\n",
    "# axs[1].set_position([boxleftspace,0.15,boxwidth,boxheight])\n",
    "\n",
    "# plt.sca(axs[0])\n",
    "# plt.errorbar(df_Resp.TTilde,df_Resp.c2_decay,xerr=[df_Resp.TTilde_err_left,df_Resp.TTilde_err_right],yerr=df_Resp.c2_decay_err,\n",
    "#              marker='s',linestyle='None',markersize=2.25,color=zs.constants.colorstyle['gray_edge'],\n",
    "#              mec=zs.constants.colorstyle['gray_edge'],mfc=zs.constants.colorstyle['gray_face'],markeredgewidth=0.5,elinewidth=0.5,label='Sonogram')\n",
    "\n",
    "\n",
    "# plt.errorbar(df_LH.TTilde,df_LH.c2,xerr=[df_LH.TTilde_err_left,df_LH.TTilde_err_right],yerr=df_LH.c2_err,\n",
    "#              marker='d',linestyle='None',markersize=2.5,color=zs.constants.colorstyle['gray_edge'],\n",
    "#              mec=zs.constants.colorstyle['gray_edge'],mfc=zs.constants.colorstyle['gray_face'],markeredgewidth=0.75,elinewidth=0.5,label='Sonogram')\n",
    "\n",
    "\n",
    "# plt.errorbar(df_Resp.TTilde,df_Resp.c2,xerr=[df_Resp.TTilde_err_left,df_Resp.TTilde_err_right],yerr=df_Resp.c2_err,\n",
    "#              marker='o',linestyle='None',markersize=2.25,color=zs.constants.colorstyle['gray_edge'],\n",
    "#              mec=zs.constants.colorstyle['gray_edge'],mfc=zs.constants.colorstyle['gray_face'],markeredgewidth=0.75,elinewidth=0.5,label='Sonogram')\n",
    "\n",
    "# plt.errorbar(TTilde_2nd,c2_2nd,yerr=c2_2nd_err,marker='o',xerr=TTilde_2nd_err,markersize=3.5,\n",
    "#              mec=zs.constants.colorstyle['green_edge'],mfc=zs.constants.colorstyle['green_face'],ecolor=zs.constants.colorstyle['green_edge'],zorder=6)\n",
    "# plt.ylabel('$c_{2}/{v_F}$',fontsize=9)\n",
    "\n",
    "# plt.ylim([-0.0125,0.135])\n",
    "\n",
    "# plt.sca(axs[1])\n",
    "\n",
    "# plt.errorbar(df_Resp.TTilde,df_Resp.D2_decay,xerr=[df_Resp.TTilde_err_left,df_Resp.TTilde_err_right],yerr=df_Resp.D2_decay_err,\n",
    "#              marker='s',linestyle='None',markersize=2.25,color=zs.constants.colorstyle['gray_edge'],\n",
    "#              mec=zs.constants.colorstyle['gray_edge'],mfc=zs.constants.colorstyle['gray_face'],markeredgewidth=0.5,elinewidth=0.5,label='Sonogram',zorder=2)\n",
    "\n",
    "# plt.errorbar(df_Resp.TTilde,df_all.D2,xerr=[df_Resp.TTilde_err_left,df_Resp.TTilde_err_right],yerr=df_Resp.D2_err,\n",
    "#              marker='o',linestyle='None',markersize=2.25,color=zs.constants.colorstyle['gray_edge'],\n",
    "#              mec=zs.constants.colorstyle['gray_edge'],mfc=zs.constants.colorstyle['gray_face'],markeredgewidth=0.5,elinewidth=0.5,label='Sonogram',zorder=1)\n",
    "\n",
    "\n",
    "# plt.errorbar(df_LH_Bin.TTilde,df_LH_Bin.D2,xerr=[df_LH_Bin.TTilde_err_left,df_LH_Bin.TTilde_err_right],yerr=df_LH_Bin.D2_err,\n",
    "#              marker='d',linestyle='None',markersize=2.5,color=zs.constants.colorstyle['gray_edge'],\n",
    "#              mec=zs.constants.colorstyle['gray_edge'],mfc=zs.constants.colorstyle['gray_face'],markeredgewidth=0.5,elinewidth=0.5,label='Local Heater',zorder=3)\n",
    "\n",
    "# plt.errorbar(TTilde_2nd,D2_2nd,yerr=D2_2nd_err,marker='o',xerr=TTilde_2nd_err,markersize=3.5,\n",
    "#              mec=zs.constants.colorstyle['green_edge'],mfc=zs.constants.colorstyle['green_face'],ecolor=zs.constants.colorstyle['green_edge'],zorder=6)\n",
    "\n",
    "# plt.xlim([0.025,0.21])\n",
    "# plt.ylim([0,7.])\n",
    "# plt.xlabel('$T/T_F$',fontsize=9)\n",
    "# plt.ylabel('$D_2$ ($\\hbar/m$)',fontsize=9,labelpad=6.5)\n",
    "\n",
    "# fig.savefig('2ndmodespeeddiffusivity.pdf',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "boxwidth=0.71\n",
    "boxheight=0.3\n",
    "boxspacing=0.05\n",
    "boxleftspace=0.25\n",
    "\n",
    "cb_width=0.015\n",
    "cb_spacing=0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "black_classic=np.array([0.15,0.15,0.15])*1.5\n",
    "black_classic_face=np.array([0.4,0.4,0.4])*1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 250x375 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs=plt.subplots(figsize=[2.5,3.75],nrows=2,ncols=1,sharex=True)\n",
    "\n",
    "axs[0].set_position([boxleftspace,0.15+boxheight+boxspacing,boxwidth,boxheight])\n",
    "axs[0].tick_params(axis='both',direction='out',top=0,right=0,bottom=1,labelsize=9,length=1.5)\n",
    "\n",
    "axs[1].tick_params(axis='both',direction='out',top=0,right=0,labelsize=9,length=1.5)\n",
    "axs[1].set_position([boxleftspace,0.15,boxwidth,boxheight])\n",
    "\n",
    "plt.sca(axs[0])\n",
    "plt.errorbar(df_Resp.TTilde,df_Resp.c2_decay,xerr=[df_Resp.TTilde_err_left,df_Resp.TTilde_err_right],yerr=df_Resp.c2_decay_err,\n",
    "             marker='o',linestyle='None',markersize=2.25,color=black_classic,\n",
    "             mec=black_classic,mfc=black_classic_face,markeredgewidth=0.5,elinewidth=0.5,label='Sonogram')\n",
    "\n",
    "\n",
    "plt.errorbar(df_LH.TTilde,df_LH.c2,xerr=[df_LH.TTilde_err_left,df_LH.TTilde_err_right],yerr=df_LH.c2_err,\n",
    "             marker='o',linestyle='None',markersize=2.5,color=black_classic,\n",
    "             mec=black_classic,mfc=black_classic_face,markeredgewidth=0.75,elinewidth=0.5,label='Sonogram')\n",
    "\n",
    "\n",
    "plt.errorbar(df_Resp.TTilde,df_Resp.c2,xerr=[df_Resp.TTilde_err_left,df_Resp.TTilde_err_right],yerr=df_Resp.c2_err,\n",
    "             marker='o',linestyle='None',markersize=2.25,color=black_classic,\n",
    "             mec=black_classic,mfc=black_classic_face,markeredgewidth=0.75,elinewidth=0.5,label='Sonogram')\n",
    "\n",
    "plt.errorbar(TTilde_2nd,c2_2nd,yerr=c2_2nd_err,marker='o',xerr=TTilde_2nd_err,markersize=3.5,\n",
    "             mec=colorstyle['red_edge'],mfc=colorstyle['red_face'],ecolor=colorstyle['red_edge'],zorder=6)\n",
    "plt.ylabel('$c_{2}/{v_F}$',fontsize=9)\n",
    "plt.ylim([-0.0125,0.135])\n",
    "\n",
    "plt.sca(axs[1])\n",
    "\n",
    "plt.errorbar(df_Resp.TTilde,df_Resp.D2_decay,xerr=[df_Resp.TTilde_err_left,df_Resp.TTilde_err_right],yerr=df_Resp.D2_decay_err,\n",
    "             marker='o',linestyle='None',markersize=2.25,color=black_classic,\n",
    "             mec=black_classic,mfc=black_classic_face,markeredgewidth=0.5,elinewidth=0.5,label='Sonogram',zorder=2)\n",
    "\n",
    "plt.errorbar(df_Resp.TTilde,df_all.D2,xerr=[df_Resp.TTilde_err_left,df_Resp.TTilde_err_right],yerr=df_Resp.D2_err,\n",
    "             marker='o',linestyle='None',markersize=2.25,color=black_classic,\n",
    "             mec=black_classic,mfc=black_classic_face,markeredgewidth=0.5,elinewidth=0.5,label='Sonogram',zorder=1)\n",
    "\n",
    "\n",
    "plt.errorbar(df_LH_Bin.TTilde,df_LH_Bin.D2,xerr=[df_LH_Bin.TTilde_err_left,df_LH_Bin.TTilde_err_right],yerr=df_LH_Bin.D2_err,\n",
    "             marker='o',linestyle='None',markersize=2.5,color=black_classic,\n",
    "             mec=black_classic,mfc=black_classic_face,markeredgewidth=0.5,elinewidth=0.5,label='Local Heater',zorder=3)\n",
    "\n",
    "plt.errorbar(TTilde_2nd,D2_2nd,yerr=D2_2nd_err,marker='o',xerr=TTilde_2nd_err,markersize=3.5,\n",
    "             mec=colorstyle['red_edge'],mfc=colorstyle['red_face'],ecolor=colorstyle['red_edge'],zorder=6)\n",
    "\n",
    "plt.xlim([0.025,0.21])\n",
    "plt.ylim([0,7.8])\n",
    "plt.xlabel('$T/T_F$',fontsize=9)\n",
    "plt.ylabel('$D_2$ ($\\hbar/m$)',fontsize=9,labelpad=6.5)\n",
    "\n",
    "fig.savefig('FigS5CD.pdf',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data1.time,data1.z,data1.dT2D\n",
    "\n",
    "\n",
    "df_array=pd.DataFrame({'t(ms)':data1.time})#,'z(um)':Sono_nTilde_show[1]\n",
    "df_array.to_clipboard(index=False,header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2D=pd.DataFrame(data1.dT2D)\n",
    "df_2D.to_clipboard(index=False,header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_evo=pd.DataFrame({'t(ms)':data1.time, 'Delta T(k_2)':data1.dT_k2})\n",
    "df_evo.to_clipboard(index=False,header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2nd=pd.DataFrame({'TTilde':np.array([TTilde_2nd]), 'c2/vF':np.array([c2_2nd]),'c2/vF_err':np.array([c2_2nd_err]),'D2':np.array([D2_2nd]),'D2_err':np.array([D2_2nd_err])})\n",
    "df_2nd.to_clipboard(index=False,header=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0923"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c2_2nd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {
    "height": "120px",
    "width": "252px"
   },
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
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